The invention relates generally to image compression, and more particularly to encoding a color image using image colorization and selected color samples.
Image compression uses a variety of encoding methods, such as Joint Photographic Experts Group (JPEG) JPEG200 encoding, to reduce information redundancies contained in an image. Image colorization is an image compression technique that recovers a color image from its corresponding grayscale image and a set of selected color samples of the color image. Generally, image colorization technique stores a grayscale version of a color image and a few color samples as representative pixels of the color image. From the color samples, image colorization technique predicts the color for the rest of the pixels of the color image.
Selecting an optimal set of color samples for colorization can be computationally costly. A selected color sample needs to represent the color range of its neighboring pixels as much as possible for reasonable performance of colorization. Existing image colorization techniques, such as machine learning based colorization, rely on a large set of color labels of representative pixels of a color image for a decoder to reconstruct the color image. A large set of color samples contributes to the large size of the reconstructed color image and a low compression rate. Additionally, selecting a large set of color samples of representative pixels is slow and hard to scale for compressing large size color images.